The present invention relates generally to diagnosis of problems with measurements from a measurement device. More specifically, the invention relates to a method for the diagnosis of measurements from measurement devices that have a relatively high data input rate and a relatively low data output rate.
Quite often, a physical property, characteristic, or phenomenon requires measurement. Various meters and measurement devices have been developed in a wide variety of industries to measure a characteristic-of-interest. For example, a person may wish to measure characteristics of the atmosphere, a fluid flow, or a moving object. Measurement devices to monitor fluid flow include ultrasonic meters, coreolis meters, magnetic flow meters, turbine meters, and orifice plates.
Measurement devices are not perfect, however. They are known to make errors in measurements, there being many reasons why measurement devices may not measure a characteristic-of-interest accurately. Furthermore, diagnosing problems with measurement devices in the field can be a difficult and troublesome experience. This is particularly true if a large amount of data (i.e. a high input data rate) is being processed into a small amount of data (i.e. a low output data rate but with a high value content). The problem is exacerbated when input data varies considerably from one field location to another, resulting in no single or small data set being representative for typical conditions in the field.
An ultrasonic meter provides a good example of a measurement device with a high input data rate and a low output data rate, where measurement data varies from one location to another.
An ultrasonic meter, such as disclosed in U.S. Pat. No. 4,646,575, hereby incorporated by reference for all purposes, can have a 100-fold or more reduction in quantity between input data and output data. For example, an ultrasonic meter typically has a spoolpiece through which there is a fluid flow. Along the perimeter of the spoolpiece are one or more sets of transducers that act as transceivers, each transducer generating an ultrasonic signal and then receiving an ultrasonic signal from the respective transducer in the transducer pair. This may happen thousands of times per minute. Transit times are thus measured along each chord (i.e. ultrasonic wave path) for the upstream and downstream ultrasonic signals. The difference in travel times between upstream and downstream ultrasonic signals indicates the velocity of the fluid flow within the pipeline.
The ultrasonic meter also includes electronics that sample and record pertinent ultrasonic signal information. Each ultrasonic signal generated by a transducer (either upstream or downstream) is identified by numerous pieces of information when sampled and recorded. These include a wavenumber, path identifier (Aup, Adwn, Bup, Bdwn, etc.), gain (AGC), hold number (delay from generation of the ultrasonic signal until the time at which the data is recorded, indicating a window during which the ultrasonic signal is expected to arrive), and a value for each of, e.g., 256 samples in the received waveform. The sample rate must also be known.
To determine transit time accurately, a batch of e.g., 20 ultrasonic signals along the same chord and same direction are taken and then processed to provide velocity and speed of sound for each chord. Thus, in this example, over ten thousand pieces of information for each chord are transformed into two: velocity and speed of sound for each chord. This information may then be averaged to compute average velocity and speed of sound for the fluid passing through the spoolpiece. This is an example of a high data input and low data output.
Unfortunately, measurement errors occur. In the case of an ultrasonic meter, one or more of the measurements may deviate so significantly from a benchmark that it indicates a problem either with the fluid flow or with the meter itself. Typically, it will be difficult to diagnose the problem based on only the velocity and speed of sound measurements. Similarly, it may be difficult to determine based only on velocity and speed of sound measurements whether there exists a measurement error or whether an unusual or notable event is occurring in the medium being measured by the measurement device.
A technician may be dispatched to the site of the measurement device to analyze the apparent problem based on the greater amount of data available at the meter location. One approach to investigating the apparent problem includes recording the raw data, partially calculated values, or final answers at predetermined moments in time. This may be referred to as inserting “measurement points” into the data sampling. Examination of the recorded data is then made and an attempt made to identify the problem. One problem with such an approach, however, is its failure to collect data in between the temporal measurement points. As a result, a substantial amount of data is not collected. If a cause or condition of a meter problem takes place while data is not being recorded, detection of the cause or condition can be missed.
Alternately, an in-circuit emulator at the meter location may be used to try and identify the apparent problem. An in-circuit emulator is a device separate from the measurement device or a programmed feature in the measurement device electronics that waits for a trigger condition (such as an unusually high maximum transit time). Upon occurrence of the trigger condition, the in-circuit emulator triggers a secondary effect—it either records data corresponding to that instant in time or stops program execution. This recorded data or program memory is then examined in an effort to identify the problem. A problem with this approach, however, is that it does not collect data prior to the trigger event. This is a problem when the trigger event is only a culmination of a trend or ongoing problem in existence before the trigger event. In such a case, it may be difficult to identify the problem by use of an in-circuit simulator.
A drawback with both of these approaches is that they rely greatly on the knowledge and training of the technical persons who are sent to investigate the problem at the meter location. There is little opportunity to carefully diagnose the problem elsewhere because much of the analysis and effort to rectify a problem takes place at the meter location.
Both of these approaches record only limited data, either at the time of an event or at spaced intervals. This is a problem because a more complete set of data is important when attempting to identify a problem with the measurement device. A problem with the measurement of fluid flow may correlate to any one of a number of different issues. A peak selection error may be due to noise, turbulence in the fluid flow, or incorrect default values in the meter's software. However, because of the high amount of data that is processed or refined down in a high input-low output processing scheme, crucial information may go unreported or underreported, leading to an inability to adequately describe fleeting anomalous conditions. If these fleeting conditions are not captured by the method used to identify a problem with the measurement device, they go undetected.